caa a22 4500 378925903 CHVBK 20180305123617.0 cr unu---uuuuu 161128e20040903xx s 000 0 eng 10.2202/1544-6115.1038 doi (NATIONALLICENCE)gruyter-10.2202/1544-6115.1038 Hierarchical Bayesian Neural Network for Gene Expression Temporal Patterns [Elektronische Daten] [Yulan Liang, Arpad G Kelemen] There are several important issues to be addressed for gene expression temporal patterns' analysis: first, the correlation structure of multidimensional temporal data; second, the numerous sources of variations with existing high level noise; and last, gene expression mostly involves heterogeneous multiple dynamic patterns. We propose a Hierarchical Bayesian Neural Network model to account for the input correlations of time course gene array data. The variations in absolute gene expression levels and the noise can be estimated with the hierarchical Bayesian setting. The network parameters and the hyperparameters were simultaneously optimized with Monte Carlo Markov Chain simulation. Results show that the proposed model and algorithm can well capture the dynamic feature of gene expression temporal patterns despite the high noise levels, the highly correlated inputs, the overwhelming interactions, and other complex features typically present in microarray data. We test and demonstrate the proposed models with yeast cell cycle temporal data sets. The model performance of Hierarchical Bayesian Neural Network was compared to other popular machine learning methods such as Nearest Neighbor, Support Vector Machine, and Self Organized Map. ©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston Statistical Theory and Methods nationallicence Microarrays nationallicence Hierarchical Bayesian Neural Networks nationallicence Heterogeneous gene expression temporal patterns nationallicence Hyper-prior nationallicence Monte Carlo Markov Chain nationallicence Liang Yulan Department of Biostatistics, University at Buffalo aut Kelemen Arpad G. Department of Computer and Information Science, Niagara University, Department of Biostatistics, University at Buffalo aut Statistical Applications in Genetics and Molecular Biology De Gruyter 3/1(2004-09-03), 1-23 3:1<1 2004 3 sagmb https://doi.org/10.2202/1544-6115.1038 text/html Onlinezugriff via DOI 1 research article jats NATIONALLICENCE 856 40 https://doi.org/10.2202/1544-6115.1038 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Liang Yulan Department of Biostatistics, University at Buffalo aut NATIONALLICENCE 700 1- Kelemen Arpad G. Department of Computer and Information Science, Niagara University, Department of Biostatistics, University at Buffalo aut NATIONALLICENCE 773 0- Statistical Applications in Genetics and Molecular Biology De Gruyter 3/1(2004-09-03), 1-23 3:1<1 2004 3 sagmb CC0 http://creativecommons.org/publicdomain/zero/1.0 nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-gruyter